When we talk about inferential statistics, p-values are often considered the main way to check if results are significant. But only looking at p-values can sometimes be confusing. That’s why it’s important to also report effect sizes. This gives us a clearer view of the results and what they really mean in the real world.
P-Values: A p-value helps us test an idea by showing the chance of getting the results we see if nothing is actually happening (that’s called the null hypothesis). For example, if a p-value is 0.05, it means there’s a 5% chance we would see these results just by random chance.
Effect Sizes: Effect sizes measure how big or strong an effect is. Instead of just telling us if something is happening (like a p-value does), effect sizes tell us how big that effect really is. For example, using a measure called Cohen's d can help us understand how important our findings are in the real world.
Understanding the Context: Effect sizes help give meaning to p-values. A tiny p-value might show something is significant, but if the effect size is very small, it might not really matter much in practice. For instance, if a new medicine shows a p-value of 0.01, but the effect size is tiny (like d = 0.1), it could mean the medicine doesn’t help patients much, even though it looks significant on paper.
Comparing Studies: Effect sizes make it easier to compare results across different studies. One study might have a significant p-value, but another study might show a bigger or smaller effect size. This helps researchers see how strong or reliable the findings are in different situations.
Avoiding Wrong Impressions: Focusing only on p-values can lead to a simple way of thinking: results are either "significant" or "not significant." But effect sizes show us that there are degrees of results. For example, if we try a new teaching method and find a p-value of 0.03 with a medium effect size (d = 0.5), it means that not only is the method effective statistically, but it also helps students in a real way.
Using effect sizes along with p-values helps tell a better story in research. It lets researchers explain their findings in a clearer way. By knowing not just if an effect exists, but also how strong it is, we can make smarter choices in research and real-life situations. So, always remember: when working with inferential statistics, look beyond p-values. Effect sizes are key to understanding what the results really mean in the real world!
When we talk about inferential statistics, p-values are often considered the main way to check if results are significant. But only looking at p-values can sometimes be confusing. That’s why it’s important to also report effect sizes. This gives us a clearer view of the results and what they really mean in the real world.
P-Values: A p-value helps us test an idea by showing the chance of getting the results we see if nothing is actually happening (that’s called the null hypothesis). For example, if a p-value is 0.05, it means there’s a 5% chance we would see these results just by random chance.
Effect Sizes: Effect sizes measure how big or strong an effect is. Instead of just telling us if something is happening (like a p-value does), effect sizes tell us how big that effect really is. For example, using a measure called Cohen's d can help us understand how important our findings are in the real world.
Understanding the Context: Effect sizes help give meaning to p-values. A tiny p-value might show something is significant, but if the effect size is very small, it might not really matter much in practice. For instance, if a new medicine shows a p-value of 0.01, but the effect size is tiny (like d = 0.1), it could mean the medicine doesn’t help patients much, even though it looks significant on paper.
Comparing Studies: Effect sizes make it easier to compare results across different studies. One study might have a significant p-value, but another study might show a bigger or smaller effect size. This helps researchers see how strong or reliable the findings are in different situations.
Avoiding Wrong Impressions: Focusing only on p-values can lead to a simple way of thinking: results are either "significant" or "not significant." But effect sizes show us that there are degrees of results. For example, if we try a new teaching method and find a p-value of 0.03 with a medium effect size (d = 0.5), it means that not only is the method effective statistically, but it also helps students in a real way.
Using effect sizes along with p-values helps tell a better story in research. It lets researchers explain their findings in a clearer way. By knowing not just if an effect exists, but also how strong it is, we can make smarter choices in research and real-life situations. So, always remember: when working with inferential statistics, look beyond p-values. Effect sizes are key to understanding what the results really mean in the real world!